This paperpresent a data-driven, unsupervised method for unusual

scene detection from static webcams. Such time-lapse

data is usually captured with very low or varying framerate.

This precludes the use of tools typically used in

surveillance (e.g., object tracking). Hence, our algorithm

is based on simple image features. This paperdefine usual scenes

based on the concept of meaningful nearest neighbours instead

of building explicit models. To effectively compare

the observations, our algorithm adapts the data representation.

Furthermore, This paperuse incremental learning techniques

to adapt to changes in the data-stream. Experiments on several

months of webcam data show that our approach detects

plausible unusual scenes, which have not been observed in

the data-stream before.